{"id":"W2114147640","doi":"10.1109/pesgm.2012.6344624","title":"Tracking energy consumptions of home appliances using electrical signature data","year":2012,"lang":"en","type":"article","venue":"","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Smart grid; Smart meter; Metering mode; Home automation; Electricity meter; Harmonics; Computer science; Energy conservation; Tracking (education); Electric potential energy; Energy (signal processing); Metre; Real-time computing; Power (physics); Embedded system; Electrical engineering; Telecommunications; Engineering; Voltage","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001211726,0.0002001899,0.0002164693,0.001022568,0.0001053664,0.0003873118,0.0001578846,0.0002380766,0.0009812929],"category_scores_gemma":[0.0007153463,0.00008249465,0.00009167539,0.0009162439,0.0001012997,0.0004544595,0.000167097,0.0001946306,0.0004087517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001497547,"about_ca_system_score_gemma":0.0001005156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006263673,"about_ca_topic_score_gemma":0.001461296,"domain_scores_codex":[0.9998468,0.00002337655,0.00001169144,0.00003262089,0.00007246226,0.00001300072],"domain_scores_gemma":[0.9996848,0.00009881887,0.00007641966,0.00004914432,0.00007626975,0.00001454009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009175895,0.0002772749,0.1240893,0.0002453977,0.00009110775,0.0005175131,0.000454441,0.02635164,0.188022,0.003143325,0.002660409,0.6532301],"study_design_scores_gemma":[0.00005251623,0.0005770077,0.2537736,0.00008135036,0.0001229225,0.001575546,0.0006904054,0.4602407,0.262749,0.006020488,0.01400209,0.0001143517],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8561718,0.0003320406,0.1284276,0.0001866341,0.00006347473,0.00005864253,0.001005259,0.001623396,0.01213114],"genre_scores_gemma":[0.9824402,0.0001751955,0.01558066,0.00001835488,0.00001136974,0.000009225681,0.0003112695,0.00003060329,0.001423051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001022568,"threshold_uncertainty_score":0.003282785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05907985420678087,"score_gpt":0.2635793518834201,"score_spread":0.2044994976766392,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}